Approximating high-dimensional dynamics by barycentric coordinates with linear programming

The increasing development of novel methods and techniques facilitates the measurement of high-dimensional time series but challenges our ability for accurate modeling and predictions. The use of a general mathematical model requires the inclusion of many parameters, which are difficult to be fitted for relatively short high-dimensional time series observed. Here, we propose a novel method to accurately model a high-dimensional time series. Our method extends the barycentric coordinates to high-dimensional phase space by employing linear programming, and allowing the approximation errors explicitly. The extension helps to produce free-running time-series predictions that preserve typical topological, dynamical, and/or geometric characteristics of the underlying attractors more accurately than the radial basis function model that is widely used. The method can be broadly applied, from helping to improve weather forecasting, to creating electronic instruments that sound more natural, and to comprehensively understanding complex biological data.

Comparison between mathematical models of intermittent androgen suppression for prostate cancer

Mathematical modelling is essential for personalizing intermittent androgen suppression, which was proposed to delay the relapse of prostate cancer by stopping and resuming the hormone therapy repeatedly than adopting the conventional continuous androgen suppression, or normal hormonal therapy. Although there are several mathematical models for intermittent androgen suppression, the performances of these mathematical models have not been compared sufficiently. In this paper, we compare the Hirata-Bruchovsky-Aihara model with the Portz-Kuang-Nagy model, two recently proposed models for intermittent androgen suppression. We fitted these mathematical models to the actual data of 17 patients and examined the dynamical behavior and prediction accuracy of these models. Although we found no significant difference between these models in terms of prediction accuracy, the Portz-Kuang-Nagy model could not reproduce the relapse under the simulation condition assuming the continuous androgen suppression. Thus, the results suggest that the Hirata-Bruchovsky-Aihara model is more useful than the Portz-Kuang-Nagy model when we attempt to compare the therapeutic efficiencies of intermittent suppression and continuous androgen suppression.

First case report of de novo ulcerative colitis developing after orthotopic liver transplantation successfully treated by granulocyte and monocyte apheresis

BACKGROUND

Immunosuppressants such as tacrolimus and cyclosporine are prescribed long-term after orthotopic liver transplantation (OLT) to prevent allograft rejection. Although these immunosuppressants are known to effectively control ulcerative colitis (UC), some post-OLT patients develop exacerbation of preexisting UC or de novo UC. Although aminosalicylates and corticosteroid courses are usually effective to treat such UC, several patients have developed uncontrollable disease and required colectomies.

CASE REPORT

We have reported a patient who developed de novo UC after OLT to treat liver cirrhosis and hepatocellular carcinoma associated with hepatitis B virus (HBV) infection. Existence of the HBV infection made us avoid to increase the corticosteroid dose or to use other immunosuppressants such as azathioprine or infliximab.

CONCLUSIONS

In this patient, granulocyte and monocyte apheresis was highly effective in terms of inducing remission of de novo UC. No adverse event was noted.

CaIrO3: a spin-orbit Mott insulator beyond the j(eff) ground state

In CaIrO3, electronic correlation, spin-orbit coupling, and tetragonal crystal field splitting are predicted to be of comparable strength. However, the nature of its ground state is still an object of debate, with contradictory experimental and theoretical results. We probe the ground state of CaIrO3 and assess the effective tetragonal crystal field splitting and spin-orbit coupling at play in this system by means of resonant inelastic x-ray scattering. We conclude that insulating CaIrO3 is not a j(eff) = 1/2 iridate and discuss the consequences of our finding to the interpretation of previous experiments. In particular, we clarify how the Mott insulating state in iridates can be readily extended beyond the j(eff) = 1/2 ground state.

Fast time-series prediction using high-dimensional data: evaluating confidence interval credibility

I propose an index for evaluating the credibility of confidence intervals for future observables predicted from high-dimensional time-series data. The index evaluates the distance from the current state to the data manifold. I demonstrate the index with artificial datasets generated from the Lorenz'96 II model [Lorenz, in Proceedings of the Seminar on Predictability, Vol. 1 (ECMWF, Reading, UK, 1996), p. 1], the Lorenz'96 I model [Hansen and Smith, 2859:TROOCI>2.0.CO;2">J. Atmos. Sci. 57, 2859 (2000).

Multiple cytokine-producing testicular malignant lymphoma with clinical symptoms resembling infectious signs

We present the case of a 64-year-old male with painful swelling of the bilateral testes and epididymides, high fever, leukocytosis, and an elevated C-reactive protein (CRP) level. This is the first case report of testicular diffuse large B-cell lymphoma, not otherwise specified (DLBCL, NOS) immunostained for multiple cytokines and their receptors, which clearly demonstrates that tumor cells express multiple cytokines [interleukin-6 (IL-6) and granulocyte colony-stimulating factor (G-CSF)] and their receptors [IL-6 receptor (IL-6R) and G-CSF receptor (G-CSFR)]. The clinical course showed that the reduction in tumor size was accompanied by a corresponding improvement in clinical symptoms and peripheral blood findings. Such clinical investigation may lead clinicians to misdiagnose inflammatory disease rather than neoplastic disease. Recognizing this paraneoplastic phenomenon associated with some cases of testicular DLBCL, NOS is important. In addition, this case suggests that the growth of tumor cells may be promoted through autocrine mechanisms of IL-6 and G-CSF, which are produced by tumor cells. The possibility that these cytokines can be produced by tumor cells and can accelerate tumor proliferation should be considered to be a cause of severe clinical symptoms, an aggressive clinical course, and an indication of the necessity of treatment. Certain cytokines may be used as tumor markers in some cases of DLBCL, NOS.

Model predictive control for optimally scheduling intermittent androgen suppression of prostate cancer

Mathematical modeling of prostate cancer under intermittent androgen suppression revealed that we may be able to delay relapse by optimally scheduling the hormone therapy for each patient. However, our previous study showed the difficulty of the scheduling by minimizing the maximal tumor growth rate because the transient dynamics is also important and can help to delay the relapse for a finite time. Here, we propose to use model predictive control for scheduling intermittent androgen suppression. We find that model predictive control tends to delay the relapse of prostate specific antigen more than the method with minimizing the maximal tumor growth rate. Therefore, model predictive control is a promising approach for practically applying the mathematical model to optimally schedule intermittent androgen suppression.

A new protocol for intermittent androgen suppression therapy of prostate cancer with unstable saddle-point dynamics

Intermittent androgen suppression (IAS) therapy is a class of hormonal treatment for prostate cancer, in which a drug-induced androgen deprivation can reduce the population of prostate cancer cells. In IAS therapy, drugs are administrated only in on-treatment periods that are separated intermittently by off-treatment periods. The presence of off-treatment periods may be beneficial for maintaining the sensitivity of the tumor cells to androgen deprivation. Thus, IAS can be superior to continuous androgen suppression (CAS) for delaying or possibly preventing relapse of a tumor. IAS therapy usually monitors the level of serum prostate-specific antigen (PSA), which is related to the population of tumor cells. Each on-treatment period begins when the PSA level is greater than an upper threshold; treatment results in a decrease in the PSA level. The on-treatment period is suspended when the PSA level falls below a lower threshold; the PSA level then rises again until the beginning of the next on-treatment period. To determine the transitions between on- and off-treatment periods, we propose a new IAS protocol that uses a model-based estimate of the state point in the phase space of the tumor dynamics. We show that the proposed protocol is effective if, in each of the on- and off-treatment periods, the tumor dynamics exhibits a saddle-point instability accompanied by a stable manifold. Mathematical analysis reveals that tumor dynamics can be controlled in a more effective and robust manner with the proposed protocol than with conventional IAS. We also discuss the clinical feasibility of the proposed protocol as an alternative to conventional IAS therapy.

Uterine endometrial carcinoma with trophoblastic differentiation: a case report with literature review

Choriocarcinoma is categorized as either gestational or nongestational depending on its origin. Nongestational choriocarcinoma originated in the trophoblastic differentiation is a rare but an aggressive tumor. This article reports a nongestational case of a uterine endometrial carcinoma with trophoblastic differentiation. A 54-year-old woman with a history of atypical genital bleeding that underwent semi-radical hysterectomy, bilateral salpingo-oophrectomy, and pelvic lymph nodes dissection. Pathological investigation showed that the tumor had endometrioid adenocarcinoma and choriocarcinomatous components. Although a series of multimodality treatments including craniotomy were performed, she died of aggressive lung and brain metastases one year after the primary surgery.

Parameter estimation and optimal scheduling algorithm for a mathematical model of intermittent androgen suppression therapy for prostate cancer

We propose an algorithm based on cross-entropy to determine parameters of a piecewise linear model, which describes intermittent androgen suppression therapy for prostate cancer. By comparing with clinical data, the parameter estimation for the switched system shows good fitting accuracy and efficiency. We further optimize switching time points for the piecewise linear model to obtain a feasible therapeutic schedule. The simulation results of therapeutic effect are superior to those of previous strategy.

Application of joint permutations for predicting coupled time series

In this work, we introduce a model for predicting multivariate time series data. This model was obtained by partitioning the state space with joint permutations. We review the theoretical framework of the previous works, show a simple extension to multivariate data, and compare its performance to the previous model obtained by permutations for predicting scalar time series data.

FTIR spectroscopy of three fundamental bands of H2F+

The ν1, ν2, and ν3 bands of H2F(+) were observed with a Fourier transform absorption spectroscopic technique in the 3 and 7 μm regions. The ion was produced with a hollow cathode discharge in a F2, He, and H2 gas mixture. A simultaneous analysis of FT data combined with laser spectroscopic data was carried out using the Watson's A-reduced Hamiltonian to determine molecular constants in vibrationally excited states. The effect of the vibration-rotation interaction between the ν1 and ν3 states was found to be small compared with the case of H2O. The vibration-rotation transitions of the ν2 band were first identified and analyzed to obtain molecular constants in the ν2 state, and the band origin was determined to be 1370.5236 (7) cm(-1) with one standard deviation in parentheses. Determined molecular constants can be used to derive the re structure of H2F(+) as re(H-F) = 0.9608(6) Å, ∠e(H-F-H) = 112.2(2)° with the error corresponding to the uncertainty of the assumed vibration rotation constant γ2(a) and the range of the values derived from three pairs of rotational constants.

Towards dynamical network biomarkers in neuromodulation of episodic migraine

Computational methods have complemented experimental and clinical neurosciences and led to improvements in our understanding of the nervous systems in health and disease. In parallel, neuromodulation in form of electric and magnetic stimulation is gaining increasing acceptance in chronic and intractable diseases. In this paper, we firstly explore the relevant state of the art in fusion of both developments towards translational computational neuroscience. Then, we propose a strategy to employ the new theoretical concept of dynamical network biomarkers (DNB) in episodic manifestations of chronic disorders. In particular, as a first example, we introduce the use of computational models in migraine and illustrate on the basis of this example the potential of DNB as early-warning signals for neuromodulation in episodic migraine.

Forecasting large aftershocks within one day after the main shock

Forecasting the aftershock probability has been performed by the authorities to mitigate hazards in the disaster area after a main shock. However, despite the fact that most of large aftershocks occur within a day from the main shock, the operational forecasting has been very difficult during this time-period due to incomplete recording of early aftershocks. Here we propose a real-time method for efficiently forecasting the occurrence rates of potential aftershocks using systematically incomplete observations that are available in a few hours after the main shocks. We demonstrate the method's utility by retrospective early forecasting of the aftershock activity of the 2011 Tohoku-Oki Earthquake of M9.0 in Japan. Furthermore, we compare the results by the real-time data with the compiled preliminary data to examine robustness of the present method for the aftershocks of a recent inland earthquake in Japan.

An approach for plasma cell myeloma diagnosis by two-color flow cytometry based on kappa/lambda ratios of CD38-gated plasma cells

Criteria from the World Health Organization (WHO) are commonly used to diagnose plasma cell myeloma (PCM), but they are complex and require several laboratory parameters. To differentiate reactive plasmacytosis from clonal plasma cell neoplasms, such as PCM, it is important to accurately determine the expression of the cytoplasmic immunoglobulin (cIg) light chain (LC). Through retrospective analyses, we selected the patients with PCM, and analyzed records of 52 PCM patients, who underwent bone biopsies, and final diagnosis of PCM was established according to WHO criteria, and 22 controls. In the present study, all samples were analyzed by flow cytometry (FC) in the side scatter vs CD38 histogram mode, and the CD38-gated plasma cell population was identified. The positive cell ratios of kappa and lambda to plasma cell populations were analyzed. PCM cells were distinguished from normal plasma cells by a cut-off level between 0.80 and 3.3, a sensitivity of 90.3 percent, and a specificity of 81.1 percent. Two-color FC analysis is simple to perform, inexpensive, and clinically relevant data are obtained soon after completion of the FC measurements. It could be one of the helpful tools in the diagnosis of PCM. The correct diagnosis of PCM can be achieved more simply, efficiently, and rapidly by combining this method.

Experimental distinction between chaotic and strange nonchaotic attractors on the basis of consistency

We experimentally study strange nonchaotic attractors (SNAs) and chaotic attractors by using a nonlinear integrated circuit driven by a quasiperiodic input signal. An SNA is a geometrically strange attractor for which typical orbits have nonpositive Lyapunov exponents. It is a difficult problem to distinguish between SNAs and chaotic attractors experimentally. If a system has an SNA as a unique attractor, the system produces an identical response to a repeated quasiperiodic signal, regardless of the initial conditions, after a certain transient time. Such reproducibility of response outputs is called consistency. On the other hand, if the attractor is chaotic, the consistency is low owing to the sensitive dependence on initial conditions. In this paper, we analyze the experimental data for distinguishing between SNAs and chaotic attractors on the basis of the consistency.

Quantifying collective attention from tweet stream

Online social media are increasingly facilitating our social interactions, thereby making available a massive “digital fossil” of human behavior. Discovering and quantifying distinct patterns using these data is important for studying social behavior, although the rapid time-variant nature and large volumes of these data make this task difficult and challenging. In this study, we focused on the emergence of “collective attention” on Twitter, a popular social networking service. We propose a simple method for detecting and measuring the collective attention evoked by various types of events. This method exploits the fact that tweeting activity exhibits a burst-like increase and an irregular oscillation when a particular real-world event occurs; otherwise, it follows regular circadian rhythms. The difference between regular and irregular states in the tweet stream was measured using the Jensen-Shannon divergence, which corresponds to the intensity of collective attention. We then associated irregular incidents with their corresponding events that attracted the attention and elicited responses from large numbers of people, based on the popularity and the enhancement of key terms in posted messages or “tweets.” Next, we demonstrate the effectiveness of this method using a large dataset that contained approximately 490 million Japanese tweets by over 400,000 users, in which we identified 60 cases of collective attentions, including one related to the Tohoku-oki earthquake. “Retweet” networks were also investigated to understand collective attention in terms of social interactions. This simple method provides a retrospective summary of collective attention, thereby contributing to the fundamental understanding of social behavior in the digital era.